Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method
Nowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/10/1744 |
| _version_ | 1797600392118272000 |
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| author | Ahmed Mahdi Obaid Amina Turki Hatem Bellaaj Mohamed Ksantini Abdulla AlTaee Alaa Alaerjan |
| author_facet | Ahmed Mahdi Obaid Amina Turki Hatem Bellaaj Mohamed Ksantini Abdulla AlTaee Alaa Alaerjan |
| author_sort | Ahmed Mahdi Obaid |
| collection | DOAJ |
| description | Nowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise big challenges for the development of tools for the early detection and diagnosis of diseases. Deep learning (DL), an area of artificial intelligence (AI), can be an informative medical tomography method that can aid in the early diagnosis of gallbladder (GB) disease based on ultrasound images (UI). Many researchers considered the classification of only one disease of the GB. In this work, we successfully managed to apply a deep neural network (DNN)-based classification model to a rich built database in order to detect nine diseases at once and to determine the type of disease using UI. In the first step, we built a balanced database composed of 10,692 UI of the GB organ from 1782 patients. These images were carefully collected from three hospitals over roughly three years and then classified by professionals. In the second step, we preprocessed and enhanced the dataset images in order to achieve the segmentation step. Finally, we applied and then compared four DNN models to analyze and classify these images in order to detect nine GB disease types. All the models produced good results in detecting GB diseases; the best was the MobileNet model, with an accuracy of 98.35%. |
| first_indexed | 2024-03-11T03:47:28Z |
| format | Article |
| id | doaj.art-3941a1bb6ded48178ab07dcfef292dc7 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T03:47:28Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-3941a1bb6ded48178ab07dcfef292dc72023-11-18T01:04:29ZengMDPI AGDiagnostics2075-44182023-05-011310174410.3390/diagnostics13101744Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical MethodAhmed Mahdi Obaid0Amina Turki1Hatem Bellaaj2Mohamed Ksantini3Abdulla AlTaee4Alaa Alaerjan5CEMLab, National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax 3029, TunisiaCEMLab, National Engineering School of Sfax, University of Sfax, Sfax 3029, TunisiaReDCAD, National Engineering School of Sfax, University of Sfax, Sfax 3029, TunisiaCEMLab, National Engineering School of Sfax, University of Sfax, Sfax 3029, TunisiaCroydon Hospital, London CR7 7YE, UKCollege of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi ArabiaNowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise big challenges for the development of tools for the early detection and diagnosis of diseases. Deep learning (DL), an area of artificial intelligence (AI), can be an informative medical tomography method that can aid in the early diagnosis of gallbladder (GB) disease based on ultrasound images (UI). Many researchers considered the classification of only one disease of the GB. In this work, we successfully managed to apply a deep neural network (DNN)-based classification model to a rich built database in order to detect nine diseases at once and to determine the type of disease using UI. In the first step, we built a balanced database composed of 10,692 UI of the GB organ from 1782 patients. These images were carefully collected from three hospitals over roughly three years and then classified by professionals. In the second step, we preprocessed and enhanced the dataset images in order to achieve the segmentation step. Finally, we applied and then compared four DNN models to analyze and classify these images in order to detect nine GB disease types. All the models produced good results in detecting GB diseases; the best was the MobileNet model, with an accuracy of 98.35%.https://www.mdpi.com/2075-4418/13/10/1744artificial intelligencedeep learningdeep neural networkultrasound imagesdiagnosisgallbladder |
| spellingShingle | Ahmed Mahdi Obaid Amina Turki Hatem Bellaaj Mohamed Ksantini Abdulla AlTaee Alaa Alaerjan Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method Diagnostics artificial intelligence deep learning deep neural network ultrasound images diagnosis gallbladder |
| title | Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method |
| title_full | Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method |
| title_fullStr | Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method |
| title_full_unstemmed | Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method |
| title_short | Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method |
| title_sort | detection of gallbladder disease types using deep learning an informative medical method |
| topic | artificial intelligence deep learning deep neural network ultrasound images diagnosis gallbladder |
| url | https://www.mdpi.com/2075-4418/13/10/1744 |
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